|
--- |
|
license: mit |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: rubert-tiny2-srl |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# rubert-tiny2-srl |
|
|
|
This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2041 |
|
- Addressee Precision: 0.7273 |
|
- Addressee Recall: 0.8 |
|
- Addressee F1: 0.7619 |
|
- Addressee Number: 10 |
|
- Benefactive Precision: 0.0 |
|
- Benefactive Recall: 0.0 |
|
- Benefactive F1: 0.0 |
|
- Benefactive Number: 1 |
|
- Causator Precision: 0.8824 |
|
- Causator Recall: 0.8333 |
|
- Causator F1: 0.8571 |
|
- Causator Number: 18 |
|
- Cause Precision: 0.6667 |
|
- Cause Recall: 0.1538 |
|
- Cause F1: 0.25 |
|
- Cause Number: 13 |
|
- Contrsubject Precision: 0.6667 |
|
- Contrsubject Recall: 0.3333 |
|
- Contrsubject F1: 0.4444 |
|
- Contrsubject Number: 6 |
|
- Deliberative Precision: 1.0 |
|
- Deliberative Recall: 0.4 |
|
- Deliberative F1: 0.5714 |
|
- Deliberative Number: 5 |
|
- Experiencer Precision: 0.7660 |
|
- Experiencer Recall: 0.8 |
|
- Experiencer F1: 0.7826 |
|
- Experiencer Number: 90 |
|
- Object Precision: 0.7576 |
|
- Object Recall: 0.6868 |
|
- Object F1: 0.7205 |
|
- Object Number: 182 |
|
- Predicate Precision: 0.9713 |
|
- Predicate Recall: 0.9967 |
|
- Predicate F1: 0.9839 |
|
- Predicate Number: 306 |
|
- Overall Precision: 0.8719 |
|
- Overall Recall: 0.8415 |
|
- Overall F1: 0.8565 |
|
- Overall Accuracy: 0.9429 |
|
|
|
## Model description |
|
|
|
More information needed |
|
|
|
## Intended uses & limitations |
|
|
|
More information needed |
|
|
|
## Training and evaluation data |
|
|
|
More information needed |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 0.00018632464179881193 |
|
- train_batch_size: 4 |
|
- eval_batch_size: 1 |
|
- seed: 755657 |
|
- gradient_accumulation_steps: 8 |
|
- total_train_batch_size: 32 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_ratio: 0.02 |
|
- num_epochs: 2 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Addressee Precision | Addressee Recall | Addressee F1 | Addressee Number | Benefactive Precision | Benefactive Recall | Benefactive F1 | Benefactive Number | Causator Precision | Causator Recall | Causator F1 | Causator Number | Cause Precision | Cause Recall | Cause F1 | Cause Number | Contrsubject Precision | Contrsubject Recall | Contrsubject F1 | Contrsubject Number | Deliberative Precision | Deliberative Recall | Deliberative F1 | Deliberative Number | Experiencer Precision | Experiencer Recall | Experiencer F1 | Experiencer Number | Object Precision | Object Recall | Object F1 | Object Number | Predicate Precision | Predicate Recall | Predicate F1 | Predicate Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:-------------------:|:----------------:|:------------:|:----------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------:|:---------------:|:-----------:|:---------------:|:---------------:|:------------:|:--------:|:------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:----------------:|:-------------:|:---------:|:-------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
|
| 0.2845 | 1.0 | 181 | 0.2356 | 0.8 | 0.8 | 0.8000 | 10 | 0.0 | 0.0 | 0.0 | 1 | 0.7895 | 0.8333 | 0.8108 | 18 | 0.0 | 0.0 | 0.0 | 13 | 0.0 | 0.0 | 0.0 | 6 | 0.0 | 0.0 | 0.0 | 5 | 0.7320 | 0.7889 | 0.7594 | 90 | 0.7740 | 0.6209 | 0.6890 | 182 | 0.9744 | 0.9935 | 0.9838 | 306 | 0.875 | 0.8098 | 0.8412 | 0.9376 | |
|
| 0.1875 | 1.99 | 362 | 0.2041 | 0.7273 | 0.8 | 0.7619 | 10 | 0.0 | 0.0 | 0.0 | 1 | 0.8824 | 0.8333 | 0.8571 | 18 | 0.6667 | 0.1538 | 0.25 | 13 | 0.6667 | 0.3333 | 0.4444 | 6 | 1.0 | 0.4 | 0.5714 | 5 | 0.7660 | 0.8 | 0.7826 | 90 | 0.7576 | 0.6868 | 0.7205 | 182 | 0.9713 | 0.9967 | 0.9839 | 306 | 0.8719 | 0.8415 | 0.8565 | 0.9429 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.28.1 |
|
- Pytorch 2.0.0+cu117 |
|
- Datasets 2.11.0 |
|
- Tokenizers 0.13.3 |
|
|
